{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.57578125\n",
      "0.573828125\n",
      "0.551953125\n",
      "0.56875\n",
      "0.565234375\n",
      "0.560546875\n",
      "0.56328125\n",
      "0.568359375\n",
      "0.573828125\n",
      "0.5640625\n",
      "0.5703125\n",
      "0.551953125\n",
      "0.58828125\n",
      "0.561328125\n",
      "0.582421875\n",
      "0.56015625\n",
      "0.566796875\n",
      "0.573046875\n",
      "0.571875\n",
      "0.56328125\n",
      "0.55859375\n",
      "0.569921875\n",
      "0.553125\n",
      "0.5640625\n",
      "0.558984375\n",
      "0.55625\n",
      "0.5671875\n",
      "0.57265625\n",
      "0.566015625\n",
      "0.57109375\n",
      "0.56171875\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import preprocessing, cross_validation, svm\n",
    "import pandas as pd\n",
    "from sklearn.decomposition import PCA\n",
    "for i in range(1,32):\n",
    "    if i<10 :\n",
    "        b='0'+str(i)\n",
    "    else: \n",
    "        b=str(i)\n",
    "    f='testfile_'+b+'.txt'\n",
    "    df=pd.read_csv(f)\n",
    "    df.drop(['user'],1,inplace=True)\n",
    "    df.drop(['video'],1,inplace=True)\n",
    "    df.drop(['wavesegment'],1,inplace=True)\n",
    "    df.drop(['combined'],1,inplace=True)\n",
    "    df.drop(['arousal'],1,inplace=True)\n",
    "    \n",
    "    X=np.array(df.drop(['valence'],1))\n",
    "    y=np.array(df['valence'])\n",
    "    X_train,X_test,y_train,y_test =cross_validation.train_test_split(X,y,test_size=0.2)\n",
    "    pca = PCA(n_components=2)# adjust yourself\n",
    "    pca.fit(X_train)\n",
    "    X_t_train = pca.transform(X_train)\n",
    "    X_t_test = pca.transform(X_test)\n",
    "    lin_clf =  svm.SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
    "        decision_function_shape='ovo', degree=3, gamma='auto', kernel='sigmoid',\n",
    "        max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
    "        tol=0.001, verbose=False)\n",
    "    lin_clf.fit(X_train, y_train) \n",
    "    accuracy=lin_clf.score(X_test,y_test)\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.598828125\n",
      "0.583984375\n",
      "0.592578125\n",
      "0.58984375\n",
      "0.580859375\n",
      "0.595703125\n",
      "0.58984375\n",
      "0.576953125\n",
      "0.585546875\n",
      "0.5984375\n",
      "0.58671875\n",
      "0.586328125\n",
      "0.59765625\n",
      "0.590625\n",
      "0.590625\n",
      "0.583984375\n",
      "0.58671875\n",
      "0.573828125\n",
      "0.601953125\n",
      "0.598828125\n",
      "0.597265625\n",
      "0.592578125\n",
      "0.603515625\n",
      "0.591796875\n",
      "0.587109375\n",
      "0.598828125\n",
      "0.593359375\n",
      "0.58828125\n",
      "0.574609375\n",
      "0.604296875\n",
      "0.58125\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,32):\n",
    "    if i<10 :\n",
    "        b='0'+str(i)\n",
    "    else: \n",
    "        b=str(i)\n",
    "    f='testfile_'+b+'.txt'\n",
    "    df=pd.read_csv(f)\n",
    "    df.drop(['user'],1,inplace=True)\n",
    "    df.drop(['video'],1,inplace=True)\n",
    "    df.drop(['wavesegment'],1,inplace=True)\n",
    "    df.drop(['combined'],1,inplace=True)\n",
    "    df.drop(['valence'],1,inplace=True)\n",
    "    X=np.array(df.drop(['arousal'],1))\n",
    "    y=np.array(df['arousal'])\n",
    "    X_train,X_test,y_train,y_test =cross_validation.train_test_split(X,y,test_size=0.2)\n",
    "    lin_clf =  svm.SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
    "        decision_function_shape='ovo', degree=3, gamma='auto', kernel='sigmoid',\n",
    "        max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
    "        tol=0.001, verbose=False)\n",
    "    lin_clf.fit(X_train, y_train) \n",
    "    accuracy=lin_clf.score(X_test,y_test)\n",
    "    print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "testfile_02.txt\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9DIBhX2HDUlMz87zrK08xPRfm7265DCidG4a+0QAA77lqLc015dy5/2yRV1S62DEYQRFx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f1c4c3ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from numpy import *\n",
    "data1=100*array([0.564453125,0.5515625,0.56015625,0.55859375,0.579296875,0.5640625,0.56210937,0.560546875,0.5703125,0.565625,\n",
    "        0.571875,0.559375,0.5648437,0.5859375,0.56914062,0.56015625,0.571484375,0.5734375,0.55898437,0.569140625,0.551953125,\n",
    "0.56875,0.562109,0.573828125,0.569140625,0.56953125,0.56328125,0.56796875,0.567578125,0.561328125,0.565625])\n",
    "data2=100*array([0.598828125,0.583984375,0.592578125,0.58984375,0.580859375,0.595703125,0.58984375,0.576953125,0.585546875,0.5984375,\n",
    "       0.58671875,0.586328125,0.59765625,0.590625,0.590625,0.583984375,0.58671875,0.573828125,0.601953125,0.598828125,\n",
    "       0.597265625,0.592578125,0.603515625,0.591796875,0.587109375,0.598828125,0.593359375,0.58828125,0.574609375,0.604296875,\n",
    "       0.58125])\n",
    "#plt.hist(data1)#, bins=np.arange(data2.min(), data2.max()+1))\n",
    "plt.plot(data1)\n",
    "plt.ylabel('valencel accuracies')\n",
    "plt.xlabel('channel')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "incompatible sizes: argument 'height' must be length 32 or scalar",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-6b92ff187b96>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;31m#plt.xticks(x, ('1', 'Fred', 'Mary', 'Sue'))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(left, height, width, bottom, hold, data, **kwargs)\u001b[0m\n\u001b[0;32m   2702\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2703\u001b[0m         ret = ax.bar(left, height, width=width, bottom=bottom, data=data,\n\u001b[1;32m-> 2704\u001b[1;33m                      **kwargs)\n\u001b[0m\u001b[0;32m   2705\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2706\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\__init__.py\u001b[0m in \u001b[0;36minner\u001b[1;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1895\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[0;32m   1896\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[1;32m-> 1897\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1898\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1899\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mbar\u001b[1;34m(self, left, height, width, bottom, **kwargs)\u001b[0m\n\u001b[0;32m   2077\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnbars\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2078\u001b[0m             raise ValueError(\"incompatible sizes: argument 'height' \"\n\u001b[1;32m-> 2079\u001b[1;33m                               \"must be length %d or scalar\" % nbars)\n\u001b[0m\u001b[0;32m   2080\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mnbars\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2081\u001b[0m             raise ValueError(\"incompatible sizes: argument 'width' \"\n",
      "\u001b[1;31mValueError\u001b[0m: incompatible sizes: argument 'height' must be length 32 or scalar"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "x = np.arange(4)\n",
    "money = [1.5e5, 2.5e6, 5.5e6, 2.0e7]\n",
    "\n",
    "\n",
    "def millions(x, pos):\n",
    "    'The two args are the value and tick position'\n",
    "    return '$%1.1fM' % (x * 1e-6)\n",
    "\n",
    "\n",
    "formatter = FuncFormatter(millions)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.yaxis.set_major_formatter(formatter)\n",
    "plt.bar(x, money)\n",
    "plt.xticks(x, ('Bill', 'Fred', 'Mary', 'Sue'))\n",
    "plt.show()"
   ]
  }
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